Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Pollination and Flower Structure

Pollination and Flower Structure

Flowers are the reproductive, seed-producing structures of angiosperms. Typically, flowers consist of sepals, petals, stamens, and carpels. Sepals and petals are the vegetative flower organs. Stamens and carpels are the reproductive organs.

Chi-square Analysis

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Author Correction: Towards clinical-level interpretation of dental panoramic radiography using an instance-guided vision-language model.

Nature biomedical engineering·2026

Same author

Loss of Ambp ameliorates steatosis progression by activating PPARα signaling in zebrafish.

The FEBS journal·2026

Same author

Glutathione metabolism-linked ferroptosis in human seminoma: a spatial multi-omics mapping study.

Redox biology·2026

Same author

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026

Same author

BMI-based metabolic syndrome Z-score and gastrointestinal cancers: A cohort study.

Medicine·2026

Same author

Towards clinical-level interpretation of dental panoramic radiography using an instance-guided vision-language model.

Nature biomedical engineering·2026

Search research articles

Related Experiment Video

Updated: Oct 10, 2025

Forced Flowering in Mandarin Trees under Phytotron Conditions

Forced Flowering in Mandarin Trees under Phytotron Conditions

Published on: March 6, 2019

Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization.

Jianqiang Lu^1,2,3, Weize Lin¹, Pingfu Chen¹

¹School College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.

Sensors (Basel, Switzerland)

|December 10, 2021

Summary

This summary is machine-generated.

This study introduces a lightweight citrus blossom recognition model using an improved YOLOv4 architecture. The new model achieves high accuracy and is significantly faster and smaller than existing deep learning models for citrus flower estimation.

Keywords:

YOLOv4 citrus flowering rate deep learning edge computing light weight

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Related Experiment Videos

Last Updated: Oct 10, 2025

Forced Flowering in Mandarin Trees under Phytotron Conditions

Forced Flowering in Mandarin Trees under Phytotron Conditions

Published on: March 6, 2019

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Area of Science:

Computer Vision
Agricultural Technology
Machine Learning

Background:

Deep learning models for citrus blossom recognition are often complex with numerous parameters.
Accurate estimation of citrus flower quantities is crucial for natural orchard management.

Purpose of the Study:

To develop a lightweight and efficient citrus flower recognition model for natural orchards.
To improve upon existing deep learning models in terms of speed and parameter count.

Main Methods:

Utilized MobileNetv3 as a feature extractor within an improved YOLOv4 framework.
Incorporated deep separable convolution for network acceleration.
Employed the Cutout data enhancement technique to improve model robustness.

Main Results:

The improved model achieved a mean Average Precision (mAP) of 84.84%.
Demonstrated a 22% reduction in size compared to YOLOv4.
Achieved approximately double the detection speed of YOLOv4.
Outperformed Faster R-CNN in memory usage and detection speed while maintaining accuracy.

Conclusions:

The proposed lightweight model offers an effective solution for citrus flower detection in natural environments.
The model's efficiency makes it suitable for edge detection applications in precision agriculture.
This approach provides a valuable reference for real-time citrus flowering monitoring.